Track: Statistical Process Control

Abstract

Social networks have become a powerful medium for communication and information sharing, influencing various aspects of society, including the organization and participation in community events such as Parkrun. As the popularity of Parkrun events continues to grow, effective monitoring and analysis of network data become crucial for event organizers to understand participant sentiments, engagement, and overall event success. In this paper, we propose the application of the Multivariate Exponentially Weighted Moving Average (MEWMA) chart for social network monitoring of Parkrun events, enabling the detection of anomalies and trends surrounding these events. Implementing the proposed approach with UK Parkrun events data and analyzing the change points highlights how tracking participants' choice of Parkrun site for running can profoundly affect event coordination and enhance the participant experience. Such analysis will assist planners in making more informed decisions regarding event logistics, resource allocation, and safety measures for Parkrun events. This research demonstrates how monitoring methods can improve event management and enhance participant experiences through data-driven insights.